AIVIS: Next Generation Vigilant Information Seeking Artificial Intelligence-based Clinical Decision Support for Sepsis

摘要 脓毒症，一种异质综合征，其特征是全身炎症，由身体的 对感染的反应是医院治疗的最昂贵和最致命的疾病，超过27万人 仅在美国就有与脓毒症相关的死亡病例。最佳脓毒症护理的基石是早期 伴随着适当的抗菌治疗的识别和循证血流动力学的使用 液体复苏和血管活性药物等治疗。而数据驱动的方法基于 机器学习(ML)在发现高维临床数据中的模式以进行预测方面显示出了希望 在住院患者中，没有经过临床验证和FDA批准的临床决定 支持(CDS)系统，可以可靠地识别有发展为脓毒症风险的患者。此外，现有的 基于ML的解决方案与提供给它们的数据的质量和离群值的存在一样好 而失恋会对他们的表现产生有害的影响。例如，有人建议， 这样的系统本质上是在监视临床医生的肩膀-使用临床行为作为表达 利用预先存在的直觉和猜疑来产生预测。因此，脓毒症的治疗是非常必要的。 预测工具，可以有效地使用常规收集的EHR数据，评估预测置信度，以及 如果需要，采取必要步骤收集更多信息，以减少预测的不确定性和 提高诊断准确率，而不会对最终用户提出太大要求。 本项目旨在评估一种新的不确定性感知脓毒症的临床实用性、安全性和有效性。 加州大学圣地亚哥分校Health和Healcisio合作设计和开发的预测系统 Inc.，一家加州大学圣迭戈分校的初创公司，专注于先进分析技术的可扩展开发和商业化 处于重症监护环境中的系统。Healcisio系统明确设计用于提高合规性 医疗补助和医疗保险服务中心(CMS)针对脓毒症的护理协议(SEP1捆绑包)和 解决了目前在确定脓毒症发病时间方面存在的延迟和变数，从而使救命 抗生素和血流动力学支持可以及时提供。维护软件质量 确保质量管理体系(QMS)将与510(K)FDA提交的文件一起开发 用于证明安全性和有效性的一揽子计划。加强医院质量改进(QI)团队 能够衡量早期识别和SEP-1捆绑符合性的影响，这是一种新的质量衡量标准 (SEP1)和因果影响分析工具。最终，开发和开发的新技术 在该项目下进行的测试将增强我们使用高级分析预测不良事件的能力， 评估患者对治疗的反应，通过快速循环在旁边优化和个性化护理 “学习型医疗体系”框架。

Abstract Sepsis, a heterogeneous syndrome characterized by whole-body inflammation caused by the body's response to an infection, is the most expensive and deadly condition treated in hospitals, with over 270,000 cases of sepsis-related deaths in the U.S. alone. The cornerstones of optimal sepsis care are early recognition accompanied by appropriate antimicrobial therapy, and use of evidence-based hemodynamic therapies such as fluid resuscitation and vasoactive medications. While data-driven approaches based on machine learning (ML) have shown promise in finding patterns in high-dimensional clinical data to forecast sepsis among hospitalized patients, there are no clinically validated and FDA-approved clinical decision support (CDS) system that can reliably identify patients at risk of developing sepsis. Moreover, existing ML-based solutions are as good as the quality of the data presented to them, and the presence of outliers and missingness can have deleterious effects on their performance. For instance, it has been suggested that such systems are essentially looking over clinician's shoulders-using clinical behavior as the expression of preexisting intuition and suspicion to generate a prediction. As such, there is a critical need for sepsis prediction tools that can effectively use the routinely collected EHR data, assess prediction confidence, and if needed, take necessary steps to gather additional information to reduce prediction uncertainty and improve diagnostic accuracy without significant demand on the end-users. This project aims to assess the clinical utility, safety, and efficacy of a novel uncertainty-aware sepsis prediction system designed and developed in collaboration between UC San Diego Health and Healcisio Inc., a UCSD start-up focused on scalable development and commercialization of advanced analytical systems in critically care settings. The Healcisio system is explicitly designed to improve compliance with the Centers for Medicaid and Medicare Services (CMS) care protocol for sepsis (the SEP1 bundle) and to address the existing delays and variabilities in determining the sepsis onset time, so that life-saving antibiotics and hemodynamic support can be delivered in a timely fashion. To maintain software quality assurance a quality management system (QMS) will be developed to accompany a 510(k) FDA submission package to demonstrate safety and effectiveness. To enhance hospital quality improvement (QI) teams’ ability to measure impact of earlier recognition and SEP-1 bundle compliance, a novel quality measure (SEP1+) and a causal impact analysis tool is introduced. Ultimately, the novel technologies developed and tested under this project will enhance our ability to use advanced analytics to predict adverse events, assess patients’ response to therapy, and optimize and personalize care at the beside through a rapid-cycle ‘learning healthcare system’ framework.